This result indicates that we can bound the bias term of the risk of the overparameterized linear regression estimator, even in the dependent and heavy-tailed setting.
Note that the last term \(ct \sigma ^2 \mathrm {tr}(C)\)  represents the variance of the risk, which converges to zero by removing correlations and controlling for them using different techniques.
This is outside our interest, but see Lemma 11 in [1]} for further details.
